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Abstract. Wavelet derived codification techniques are widespread used in
image codifiers. The wavelet based compression methods are adequate for representing
transients. In this paper we explore the use of the discrete wavelet
transform analysis of biological signals in order to improve the data compression
capability of data ...»»»»

Abstract. Wavelet derived codification techniques are widespread used in
image codifiers. The wavelet based compression methods are adequate for representing
transients. In this paper we explore the use of the discrete wavelet
transform analysis of biological signals in order to improve the data compression
capability of data coders. The wavelet analysis provides a subband decomposition
of any signal, and this enables a loss less or a lossy implementation
with the same architecture. The signals could range from speech to sounds or
music, but the approach is more orientated to other biosignals like medical signals
EEG, ECG or discrete series. Experimental results based on wavelet coefficients
quantification, show a lossless compression of 2: I in all kind of signals,
with a fidelity, measured using PSNR, from 79dB to IOOdB, and lossy results
preserving most of the signal waveform, with a compression ratio from 3: I to
5: I, with a fidelity from 25dB to 35 dB.^^^^

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